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Explicitly replace "import tensorflow" with "tensorflow.compat.v1"
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Deepmind Team
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Diego de Las Casas
Diego de Las Casas
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# AlphaFold
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This package provides an implementation of the contact prediction network,
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associated model weights and CASP13 dataset as published in Nature.
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Any publication that discloses findings arising from using this source code must
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cite *AlphaFold: Protein structure prediction using potentials from deep
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learning* by Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick,
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Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson,
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Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan,
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Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis.
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## Setup
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### Dependencies
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* Python 3.6+.
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* [Abseil 0.8.0+](https://github.com/abseil/abseil-py)
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* [Numpy 1.16+](https://numpy.org)
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* [Six 1.12+](https://pypi.org/project/six/)
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* [Sonnet 1.35+](https://github.com/deepmind/sonnet)
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* [TensorFlow 1.14](https://tensorflow.org). Not compatible with TensorFlow
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2.0+.
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* [TensorFlow Probability 0.7.0](https://www.tensorflow.org/probability)
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You can set up Python virtual environment with these dependencies inside the
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forked `deepmind_research` repository using:
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```shell
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python3 -m venv alphafold_venv
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source alphafold_venv/bin/activate
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pip install -r alphafold_casp13/requirements.txt
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```
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### Input data
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The dataset can be downloaded from
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[Google Cloud Storage](https://console.cloud.google.com/storage/browser/alphafold_casp13_data).
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Download it e.g. using `wget`:
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```shell
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wget https://storage.googleapis.com/alphafold_casp13_data/casp13_data.zip
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```
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The zip file contains 1 directory for each CASP13 target and a `LICENSE.md`
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file. Each target directory contains the following files:
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1. `TARGET.tfrec` file. This is a
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[TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) file
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with serialized tf.train.Example protocol buffers that contain the features
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needed to run the model.
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1. `contacts/TARGET.pickle` file(s) with the predicted distogram.
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1. `contacts/TARGET.rr` file(s) with the contact map derived from the predicted
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distogram. The RR format is described on the
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[CASP website](http://predictioncenter.org/casp13/index.cgi?page=format#RR).
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Note that for **T0999** the target was manually split based on hits in HHSearch
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into 5 sub-targets, hence there are 5 distograms
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(`contacts/T0999s{1,2,3,4,5}.pickle`) and 5 RR files
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(`contacts/T0999s{1,2,3,4,5}.rr`).
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The `contacts/` folder is not needed to run the model, these files are included
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only for convenience so that you don't need to run the inference for CASP13
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targets to get the contact map.
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### Model checkpoints
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The model checkpoints can be downloaded from
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[Google Cloud Storage](https://console.cloud.google.com/storage/browser/alphafold_casp13_data).
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Download them e.g. using `wget`:
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```shell
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wget https://storage.googleapis.com/alphafold_casp13_data/alphafold_casp13_weights.zip
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```
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The zip file contains:
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1. A directory `873731`. This contains the weights for the distogram model.
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1. A directory `916425`. This contains the weights for the background distogram
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model.
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1. A directory `941521`. This contains the weights for the torsion model.
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1. `LICENSE.md`. The model checkpoints have a non-commercial license which is
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defined in this file.
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Each directory with model weights contains a number of different model
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configurations. Each model has a config file and associated weights. There is
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only one torsion model. Each model directory also contains a stats file that is
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used for feature normalization specific to that model.
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## Distogram prediction
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### Running the system
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You can use the `run_eval.sh` script to run the entire Distogram prediction
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system. There are a few steps you need to start with:
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1. Download the input data as described above. Unpack the data in the
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directory with the code.
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1. Download the model checkpoints as described above. Unpack the data.
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1. In `run_eval.sh` set the following:
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* `DISTOGRAM_MODEL` to the path to the directory with the distogram model.
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* `BACKGROUND_MODEL` to the path to the directory with the background
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model.
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* `TORSION_MODEL` to the path to the directory with the torsion model.
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* `TARGET` to the path to the directory with the target input data.
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Then run `alphafold_casp13/run_eval.sh` from the `deepmind_research` parent
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directory (you will get errors if you try running `run_eval.sh` directly from
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the `alphafold_casp13` directory).
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The contact prediction works in the following way:
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1. 4 replicas (by *replica* we mean a configuration file describing the network
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architecture and a snapshot with the network weights), each with slightly
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different model configuration, are launched to predict the distogram.
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1. 4 replicas, each with slightly different model configuration are launched to
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predict the background distogram.
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1. 1 replica is launched to predict the torsions.
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1. The predictions from the different replicas are averaged together using
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`ensemble_contact_maps.py`.
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1. The predictions for the 64 × 64 distogram crops are pasted together using
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`paste_contact_maps.py`.
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When running `run_eval.sh` the output has the following directory structure:
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* **distogram/**: Contains 4 subfolders, one for each replica. Each of these
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contain the predicted ASA, secondary structure and a pickle file with the
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distogram for each crop. It also contains an `ensemble` directory with the
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ensembled distograms.
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* **background_distogram/**: Contains 4 subfolders, one for each replica. Each
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of these contain a pickle file with the background distogram for each crop.
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It also contains an `ensemble` directory with the ensembled background
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distograms.
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* **torsion/**: Contains 1 subfolder as there was only a single replica. This
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folder contains contains the predicted ASA, secondary structure, backbone
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torsions and a pickle file with the distogram for each crop. It also
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contains an `ensemble` directory with the ensembled torsions.
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* **pasted/**: Contains distograms obtained from the ensembled distograms by
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pasting. An RR contact map file is computed from this pasted distogram.
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**This is the final distogram that was used in the subsequent AlphaFold
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folding pipeline in CASP13.**
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## Data splits
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We used a version of [PDB](https://www.rcsb.org/) downloaded on 2018-03-15. The
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train/test split can be found in the `train_domains.txt` and `test_domains.txt`
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files.
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Disclaimer: This is not an official Google product.
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